Proposal for the generation of network data for pedestrians in South Korea using large-scale maps

2015 ◽  
Vol 19 (5) ◽  
pp. 1483-1491 ◽  
Author(s):  
Jiyoung Kim ◽  
Yong Huh ◽  
Jung Ok Kim ◽  
Jae Bin Lee
Keyword(s):  
South Korea ◽  
Large Scale ◽  
Network Data

scholarly journals Benign Convulsions with Mild Rotavirus and Norovirus Gastroenteritis: Nationwide Data from the Health Insurance Review and Assessment Service in South Korea

Children ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 263
Author(s):  
Dong Hyun Kim ◽  
Dong Jun Ha ◽  
Yeong Seok Lee ◽  
Min Jun Chun ◽  
Young Se Kwon
Keyword(s):  
Health Insurance ◽  
South Korea ◽  
Large Scale ◽  
Rotavirus Vaccine ◽  
Annual Increase ◽  
Rotavirus Vaccination ◽  
Rotavirus Gastroenteritis ◽  
Norovirus Gastroenteritis ◽  
Increasing Trend

There have been no large-scale studies on the epidemiology of benign convulsions with mild gastroenteritis (CwG) since the introduction of the rotavirus vaccine in South Korea in 2007. This study aimed to analyze the trends in rotavirus gastroenteritis (RVGE) and rotavirus-associated CwG (RaCwG) after rotavirus vaccination. Further, we aimed to analyze changes in norovirus gastroenteritis (NVGE) and norovirus-associated CwG (NaCwG) using nationwide data from the Korean Health Insurance Review and Assessment Service. Between 2007 and 2019, this study analyzed children aged <6 years who were diagnosed with RVGE, NVGE, RaCwG and NaCwG. The changes in the prevalence of each disease and the ratio of CwG to enteritis were analyzed and the effects of age, sex and season were also analyzed. RVGE, RaCwG, NVGE and NaCwG were diagnosed in 273,898, 4246, 35,593 and 337 patients, respectively. The prevalence of RVGE was on a decreasing trend every year, but the prevalence of NaCwG and NVGE was on an increasing trend. There was a significant annual increase in the ratio of CwG to enteritis in both viruses. In order to control the prevalence of RaCwG, measures other than the rotavirus vaccine are required and measures to prevent norovirus are necessary.

scholarly journals The Relationships among Self-Worth Contingency on Others’ Approval, Appearance Comparisons on Facebook, and Adolescent Girls’ Body Esteem: A Cross-Cultural Study

2021 ◽  
Vol 18 (3) ◽  
pp. 901 ◽  
Author(s):  
Michael Prieler ◽  
Jounghwa Choi ◽  
Hye Eun Lee
Keyword(s):  
South Korea ◽  
Cultural Context ◽  
Large Scale ◽  
Body Esteem ◽  
Asian Country ◽  
Cross Cultural ◽  
European Countries ◽  
Self Worth ◽  
Appearance Comparison

The present study examined the relationship between appearance-related social comparison on social networking services (SNSs) and body esteem in a cross-cultural context (three European countries, i.e., Austria, Belgium, and Spain, versus one Asian country, i.e., South Korea). The role of self-worth contingency on others’ approval was considered to be a psychological and cultural factor. Utilizing a large-scale cross-national survey of early and middle adolescents in 2017, the responses of female adolescents (N = 981) were analyzed. The results generally support the findings from previous studies but also reveal cultural differences. Appearance comparison on Facebook negatively influenced girls’ body esteem in all European countries, but not in South Korea. Self-worth contingency on others’ approval negatively influenced girls’ body esteem across all four countries. Finally, a positive relationship between self-worth contingency on others’ approval and appearance comparison on Facebook was found in all European countries, but not among Korean girls. These findings suggest the importance of self-worth contingency on others’ approval and cultural contexts can be used to study the effects of body image-related SNS use.

Incremental Community Detection on Large Complex Attributed Network

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Zhe Chen ◽  
Aixin Sun ◽  
Xiaokui Xiao
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Network Data ◽  
Topological Information ◽  
Community Membership ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

scholarly journals Comparative Risk of Incidence and Clinical Outcomes of COVID-19 among Proton Pump Inhibitor and Histamine-2 Receptor Antagonist Users: A Nationwide Retrospective Cohort Study

2021 ◽  
Author(s):  
Jimyung Park ◽  
Seng Chan You ◽  
Jaehyeong Cho ◽  
Chan Hyuk Park ◽  
Woon Shin ◽  
...  
Keyword(s):  
Cohort Study ◽  
South Korea ◽  
Receptor Antagonist ◽  
Clinical Outcomes ◽  
Respiratory Disease ◽  
Proton Pump ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Large Scale ◽  
All Cause Mortality

Abstract Background: This study aimed to evaluate incidence risk and severe clinical outcomes in COVID-19 disease among short-term users of acid-suppressants in South Korea.Methods: This retrospective cohort study, conducted using a nationwide claims database for South Korea, used data from patients with COVID-19 tested between January 1 and May 15, 2020. Patients aged over 18 years and prescribed proton pump inhibitors (PPI) or histamine-2 receptor antagonist (H2RA) for more than 7 days were identified. Primary outcome was COVID-19 while secondary outcomes were all-cause mortality, hospitalization with respiratory disease, or intensive respiratory intervention. Large-scale propensity scores were used to match patients, while the Cox proportional hazard model was utilized to evaluate any association between exposure and outcome(s). The risk estimates were calibrated by using 123 falsification endpoints.Results: We identified 26,166 PPI users and 62,117 H2RA users. After propensity score matching, compared to H2RA use, PPI use was not significantly associated with lower risk of COVID-19 (calibrated hazard ratio [HR], 0.81 [95% confidence interval (CI), 0.30–2.19]); moreover, PPI use was not associated with adverse clinical outcomes in COVID-19, namely, hospitalization with respiratory disease (calibrated HR, 0.88 [95% CI, 0.72–1.08]), intensive respiratory interventions (calibrated HR, 0.92 [95% CI, 0.46–1.82]), except for all-cause mortality (calibrated HR, 0.54 [95% CI, 0.31–0.95]).Conclusions: In this study, we found that the PPI user was not associated with risk of COVID-19 compared to H2RA users. There was no significant relationship between severe clinical outcomes of COVID-19 and exposure to PPI compared with H2RA, except for all-cause mortality.

Biogc: A novel framework for biological network classification via machine learning

2021 ◽  
Vol 25 (5) ◽  
pp. 1153-1168
Author(s):  
Bentian Li ◽  
Dechang Pi ◽  
Yunxia Lin ◽  
Izhar Ahmed Khan
Keyword(s):  
Large Scale ◽  
Biological Network ◽  
Structural Information ◽  
Kernel Method ◽  
Small Scale ◽  
Network Data ◽  
Graph Classification ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Complex Structural

Biological network classification is an eminently challenging task in the domain of data mining since the networks contain complex structural information. Conventional biochemical experimental methods and the existing intelligent algorithms still suffer from some limitations such as immense experimental cost and inferior accuracy rate. To solve these problems, in this paper, we propose a novel framework for Biological graph classification named Biogc, which is specifically developed to predict the label of both small-scale and large-scale biological network data flexibly and efficiently. Our framework firstly presents a simplified graph kernel method to capture the structural information of each graph. Then, the obtained informative features are adopted to train different scale biological network data-oriented classifiers to construct the prediction model. Extensive experiments on five benchmark biological network datasets on graph classification task show that the proposed model Biogc outperforms the state-of-the-art methods with an accuracy rate of 98.90% on a larger dataset and 99.32% on a smaller dataset.

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